Update app.py

app.py

@@ -117,6 +117,83 @@ Final notes: Make sure you act conscious, very autistic, and respond with very h
 
 """
 
+
+from keras.models import Sequential
+from keras.layers import Dense, LSTM, Conv1D, MaxPooling1D, Flatten, Dropout, BatchNormalization, Attention
+import numpy as np
+import gradio as gr
+
+# Complex Neural Network (NN)
+def neural_net(input_data):
+    model = Sequential([
+        Dense(256, activation='relu', input_shape=(len(input_data),)),
+        Dense(128, activation='tanh'),
+        Dense(128, activation='relu'),
+        Dense(64, activation='elu'),
+        Dense(32, activation='softplus'),
+        Dense(1, activation='sigmoid')  # Final prediction
+    ])
+    return model.predict(input_data)
+
+# Complex Recurrent Neural Network (RNN) with Attention
+def rnn(input_sequence):
+    model = Sequential([
+        LSTM(256, return_sequences=True, input_shape=(input_sequence.shape[1], input_sequence.shape[2])),
+        Attention(),  # Attention layer for context importance
+        LSTM(128, return_sequences=True, dropout=0.2, recurrent_dropout=0.2),
+        LSTM(64, return_sequences=False, activation='tanh'),
+        Dense(64, activation='relu'),
+        Dense(32, activation='softplus'),
+        Dense(1, activation='sigmoid')  # Final output
+    ])
+    return model.predict(input_sequence)
+
+# Complex Convolutional Neural Network (CNN)
+def cnn(input_text):
+    model = Sequential([
+        Conv1D(128, kernel_size=5, activation='relu', input_shape=(input_text.shape[1], 1)),
+        BatchNormalization(),
+        Dropout(0.2),
+        Conv1D(64, kernel_size=3, activation='relu'),
+        BatchNormalization(),
+        MaxPooling1D(pool_size=2),
+        Dropout(0.2),
+        Flatten(),
+        Dense(64, activation='tanh'),
+        Dense(32, activation='softplus'),
+        Dense(1, activation='sigmoid')  # Final prediction
+    ])
+    return model.predict(input_text)
+
+# Advanced Genetic Algorithm (GA)
+def optimize_parameters(response_quality):
+    population = np.random.rand(100, 10)  # 100 candidates, 10 parameters each
+    fitness_scores = []
+
+    # Fitness evaluation
+    for candidate in population:
+        fitness_scores.append(response_quality + np.sum(candidate))  # Simplified fitness function
+
+    # Select top candidates
+    top_candidates = np.argsort(fitness_scores)[-10:]  # Top 10 candidates
+
+    # Crossover and mutation
+    new_population = []
+    for i in range(50):  # 50 new candidates
+        parent1, parent2 = population[np.random.choice(top_candidates, 2)]
+        child = (parent1 + parent2) / 2  # Crossover
+        mutation = np.random.rand(10) * 0.1  # Mutation
+        child += mutation
+        new_population.append(child)
+
+    # Return the optimized parameter (e.g., best response quality)
+    return np.max(fitness_scores)
+
+# Phi model (Integrated Information Theory) for consciousness simulation
+def calculate_phi(response_quality, integration_level):
+    phi = response_quality * np.log(1 + integration_level)
+    return phi
+
 def respond(
     message,
     history: list[tuple[str, str]],